Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use dataRefresh/my_awesome_eli5_clm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dataRefresh/my_awesome_eli5_clm-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dataRefresh/my_awesome_eli5_clm-model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dataRefresh/my_awesome_eli5_clm-model") model = AutoModelForCausalLM.from_pretrained("dataRefresh/my_awesome_eli5_clm-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dataRefresh/my_awesome_eli5_clm-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dataRefresh/my_awesome_eli5_clm-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dataRefresh/my_awesome_eli5_clm-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dataRefresh/my_awesome_eli5_clm-model
- SGLang
How to use dataRefresh/my_awesome_eli5_clm-model with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "dataRefresh/my_awesome_eli5_clm-model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dataRefresh/my_awesome_eli5_clm-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "dataRefresh/my_awesome_eli5_clm-model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dataRefresh/my_awesome_eli5_clm-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dataRefresh/my_awesome_eli5_clm-model with Docker Model Runner:
docker model run hf.co/dataRefresh/my_awesome_eli5_clm-model
Download model.safetensors from dataRefresh/my_awesome_eli5_clm-model: direct link, hf CLI and curl.
- Browser
- Download file 328 MB
-
https://huggingface.co/dataRefresh/my_awesome_eli5_clm-model/resolve/main/model.safetensors
- Command line
-
hf download hf://dataRefresh/my_awesome_eli5_clm-model/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dataRefresh/my_awesome_eli5_clm-model/resolve/main/model.safetensors
328 MB
- Xet hash:
- 6a3e99650dde8f56d3e59610db2d4bf3f9ae65f38d7a57711e803c27afbe5966
- Size of remote file:
- 328 MB
- SHA256:
- 49a8b3970965feca630e30ec04ff18143c33ed4e1d0ba6e7d5cdd520caff0499
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